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Approches d'analyse distributionnelle pour améliorer la désambiguïsation sémantique (1702.08451v1)

Published 27 Feb 2017 in cs.CL

Abstract: Word sense disambiguation (WSD) improves many NLP applications such as Information Retrieval, Machine Translation or Lexical Simplification. WSD is the ability of determining a word sense among different ones within a polysemic lexical unit taking into account the context. The most straightforward approach uses a semantic proximity measure between the word sense candidates of the target word and those of its context. Such a method very easily entails a combinatorial explosion. In this paper, we propose two methods based on distributional analysis which enable to reduce the exponential complexity without losing the coherence. We present a comparison between the selection of distributional neighbors and the linearly nearest neighbors. The figures obtained show that selecting distributional neighbors leads to better results.

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Authors (2)
  1. Mokhtar Billami (2 papers)
  2. Núria Gala (1 paper)
Citations (1)

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